OpenRaiser/Intern-Atlas huggingface dataset:
Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
Venue: arXiv cs.SE 2020
Year: 2020

Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission's AI high-level expert group and established procedures from software engineering and raise questions for future work.
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble
Venue: arXiv cs.CV 2020
Year: 2020

In general, sufficient data is essential for the better performance and generalization of deep-learning models. However, lots of limitations(cost, resources, etc.) of data collection leads to lack of enough data in most of the areas. In addition, various domains of each data sources and licenses also lead to difficulties in collection of sufficient data. This situation makes us hard to utilize not only the pre-trained model, but also the external knowledge. Therefore, it is important to leverage small dataset effectively for achieving the better performance. We applied some techniques in three aspects: data, loss function, and prediction to enable training from scratch with less data. With these methods, we obtain high accuracy by leveraging ImageNet data which consist of only 50 images per class. Furthermore, our model is ranked 4th in Visual Inductive Printers for Data-Effective Computer Vision Challenge.
Wreath/cascade products and related decomposition results for the concurrent setting of Mazurkiewicz traces (extended version)
Venue: arXiv cs.FL 2020
Year: 2020

We develop a new algebraic framework to reason about languages of Mazurkiewicz traces. This framework supports true concurrency and provides a non-trivial generalization of the wreath product operation to the trace setting. A novel local wreath product principle has been established. The new framework is crucially used to propose a decomposition result for recognizable trace languages, which is an analogue of the Krohn-Rhodes theorem. We prove this decomposition result in the special case of acyclic architectures and apply it to extend Kamp's theorem to this setting. We also introduce and analyze distributed automata-theoretic operations called local and global cascade products. Finally, we show that aperiodic trace languages can be characterized using global cascade products of localized and distributed two-state reset automata.
Regulating Safety and Security in Autonomous Robotic Systems
Venue: arXiv cs.CY 2020
Year: 2020

Autonomous Robotics Systems are inherently safety-critical and have complex safety issues to consider (for example, a safety failure can lead to a safety failure). Before they are deployed, these systems of have to show evidence that they adhere to a set of regulator-defined rules for safety and security. Formal methods provide robust approaches to proving a system obeys given rules, but formalising (usually natural language) rules can prove difficult. Regulations specifically for autonomous systems are still being developed, but the safety rules for a human operator are a good starting point when trying to show that an autonomous system is safe. For applications of autonomous systems like driverless cars and pilotless aircraft, there are clear rules for human operators, which have been formalised and used to prove that an autonomous system obeys some or all of these rules. However, in the space and nuclear sectors applications are more likely to differ, so a set of general safety principles has developed. This allows novel applications to be assessed for their safety, but are difficult to formalise. To improve this situation, we are collaborating with regulators and the community in the space and nuclear sectors to develop guidelines for autonomous and robotic systems that are amenable to robust (formal) verification. These activities also have the benefit of bridging the gaps in knowledge within both the space or nuclear communities and academia.
On the Generation, Structure, and Semantics of Grammar Patterns in Source Code Identifiers
Venue: arXiv cs.SE 2020
Year: 2020

Identifiers make up a majority of the text in code. They are one of the most basic mediums through which developers describe the code they create and understand the code that others create. Therefore, understanding the patterns latent in identifier naming practices and how accurately we are able to automatically model these patterns is vital if researchers are to support developers and automated analysis approaches in comprehending and creating identifiers correctly and optimally. This paper investigates identifiers by studying sequences of part-of-speech annotations, referred to as grammar patterns. This work advances our understanding of these patterns and our ability to model them by 1) establishing common naming patterns in different types of identifiers, such as class and attribute names; 2) analyzing how different patterns influence comprehension; and 3) studying the accuracy of state-of-the-art techniques for part-of-speech annotations, which are vital in automatically modeling identifier naming patterns, in order to establish their limits and paths toward improvement. To do this, we manually annotate a dataset of 1,335 identifiers from 20 open-source systems and use this dataset to study naming patterns, semantics, and tagger accuracy.
SafeRESTScript: Statically Checking REST API Consumers
Venue: arXiv cs.SE 2020
Year: 2020

Consumption of REST services has become a popular means of invoking code provided by third parties, particularly in web applications. Nowadays programmers of web applications can choose TypeScript over JavaScript to benefit from static type checking that enables validating calls to local functions or to those provided by libraries. Errors in calls to REST services, however, can only be found at run-time. In this paper, we present SafeRESTScript (SRS, for short) a language that extends the support of static analysis to calls to REST services, with the ability to statically find common errors such as missing or invalid data in REST calls and misuse of the results from such calls. SafeRESTScript features a syntax similar to JavaScript and is equipped with (i) a rich collection of types (including objects, arrays and refinement types)and (ii) primitives to natively support REST calls that are statically validated against specifications of the corresponding APIs. Specifications are written in HeadREST, a language that also features refinement types and supports the description of semantic aspects of REST APIs in a style reminiscent of Hoare triples. We present SafeRESTScript and its validation system, based on a general-purpose verification tool (Boogie). The evaluation of SafeRESTScript and of the prototype implementations for its validator, available in the form of an Eclipse plugin, is also discussed.
Rapid mixing from spectral independence beyond the Boolean domain
Venue: arXiv cs.DS 2020
Year: 2020

We extend the notion of spectral independence (introduced by Anari, Liu, and Oveis Gharan [ALO20]) from the Boolean domain to general discrete domains. This property characterises distributions with limited correlations, and implies that the corresponding Glauber dynamics is rapidly mixing. As a concrete application, we show that Glauber dynamics for sampling proper $q$-colourings mixes in polynomial-time for the family of triangle-free graphs with maximum degree $Δ$ provided $q\ge (α^*+δ)Δ$ where $α^*\approx 1.763$ is the unique solution to $α^*=\exp(1/α^*)$ and $δ>0$ is any constant. This is the first efficient algorithm for sampling proper $q$-colourings in this regime with possibly unbounded $Δ$. Our main tool of establishing spectral independence is the recursive coupling by Goldberg, Martin, and Paterson [GMP05].
The Sparse Hausdorff Moment Problem, with Application to Topic Models
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of identifying, from its first $m$ noisy moments, a probability distribution on $[0,1]$ of support $k<\infty$. This is equivalent to the problem of learning a distribution on $m$ observable binary random variables $X_1,X_2,\dots,X_m$ that are iid conditional on a hidden random variable $U$ taking values in $\{1,2,\dots,k\}$. Our focus is on accomplishing this with $m=2k$, which is the minimum $m$ for which verifying that the source is a $k$-mixture is possible (even with exact statistics). This problem, so simply stated, is quite useful: e.g., by a known reduction, any algorithm for it lifts to an algorithm for learning pure topic models. We give an algorithm for identifying a $k$-mixture using samples of $m=2k$ iid binary random variables using a sample of size $\left(1/w_{\min}\right)^2 \cdot\left(1/ζ\right)^{O(k)}$ and post-sampling runtime of only $O(k^{2+o(1)})$ arithmetic operations. Here $w_{\min}$ is the minimum probability of an outcome of $U$, and $ζ$ is the minimum separation between the distinct success probabilities of the $X_i$s. Stated in terms of the moment problem, it suffices to know the moments to additive accuracy $w_{\min}\cdotζ^{O(k)}$. It is known that the sample complexity of any solution to the identification problem must be at least exponential in $k$. Previous results demonstrated either worse sample complexity and worse $O(k^c)$ runtime for some $c$ substantially larger than $2$, or similar sample complexity and much worse $k^{O(k^2)}$ runtime.
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation
Venue: arXiv cs.SI 2020
Year: 2020

Personal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users' personal values, and developing effective models to predict their personal values using their Facebook data. These models leverage on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations among personal values. The LIWC word categories are adapted to non-English word use in Singapore. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross-stitch layer model. Through experiments, we show that our proposed model predicts personal values with considerable improvement of accuracy over the previous works. Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
Efficient Full Image Interactive Segmentation by Leveraging Within-image Appearance Similarity
Venue: arXiv cs.CV 2020
Year: 2020

We propose a new approach to interactive full-image semantic segmentation which enables quickly collecting training data for new datasets with previously unseen semantic classes (A demo is available at https://youtu.be/yUk8D5gEX-o). We leverage a key observation: propagation from labeled to unlabeled pixels does not necessarily require class-specific knowledge, but can be done purely based on appearance similarity within an image. We build on this observation and propose an approach capable of jointly propagating pixel labels from multiple classes without having explicit class-specific appearance models. To enable long-range propagation, our approach first globally measures appearance similarity between labeled and unlabeled pixels across the entire image. Then it locally integrates per-pixel measurements which improves the accuracy at boundaries and removes noisy label switches in homogeneous regions. We also design an efficient manual annotation interface that extends the traditional polygon drawing tools with a suite of additional convenient features (and add automatic propagation to it). Experiments with human annotators on the COCO Panoptic Challenge dataset show that the combination of our better manual interface and our novel automatic propagation mechanism leads to reducing annotation time by more than factor of 2x compared to polygon drawing. We also test our method on the ADE-20k and Fashionista datasets without making any dataset-specific adaptation nor retraining our model, demonstrating that it can generalize to new datasets and visual classes.
Challenge report:VIPriors Action Recognition Challenge
Venue: arXiv cs.CV 2020
Year: 2020

This paper is a brief report to our submission to the VIPriors Action Recognition Challenge. Action recognition has attracted many researchers attention for its full application, but it is still challenging. In this paper, we study previous methods and propose our method. In our method, we are primarily making improvements on the SlowFast Network and fusing with TSM to make further breakthroughs. Also, we use a fast but effective way to extract motion features from videos by using residual frames as input. Better motion features can be extracted using residual frames with SlowFast, and the residual-frame-input path is an excellent supplement for existing RGB-frame-input models. And better performance obtained by combining 3D convolution(SlowFast) with 2D convolution(TSM). The above experiments were all trained from scratch on UCF101.
More than Code: Contributions in Scrum Software Engineering Teams
Venue: arXiv cs.SE 2020
Year: 2020

Motivated and competent team members are a vital part of Agile Software development and make or break any project's success. Motivation is fostered by continuous progress and recognition of efforts. These concepts are founding pillars of the Scrum methodology, which focuses on self-organizing teams. The types of contributions Scrum development team members make to a project's progress are not only technical. However, a comprehensive model comprising the varied contributions in modern software engineering teams is not yet established. We propose a model that incorporates contributions of all Scrum roles, explicitly including those which are not directly related to project artifacts. It improves the visibility of performed tasks, acts as a starting point for team retrospection, and serves as a foundation for discussion in the research community.
Improved Deterministic Network Decomposition
Venue: arXiv cs.DS 2020
Year: 2020

Network decomposition is a central tool in distributed graph algorithms. We present two improvements on the state of the art for network decomposition, which thus lead to improvements in the (deterministic and randomized) complexity of several well-studied graph problems. - We provide a deterministic distributed network decomposition algorithm with $O(\log^5 n)$ round complexity, using $O(\log n)$-bit messages. This improves on the $O(\log^7 n)$-round algorithm of Rozhoň and Ghaffari [STOC'20], which used large messages, and their $O(\log^8 n)$-round algorithm with $O(\log n)$-bit messages. This directly leads to similar improvements for a wide range of deterministic and randomized distributed algorithms, whose solution relies on network decomposition, including the general distributed derandomization of Ghaffari, Kuhn, and Harris [FOCS'18]. - One drawback of the algorithm of Rozhoň and Ghaffari, in the $\mathsf{CONGEST}$ model, was its dependence on the length of the identifiers. Because of this, for instance, the algorithm could not be used in the shattering framework in the $\mathsf{CONGEST}$ model. Thus, the state of the art randomized complexity of several problems in this model remained with an additive $2^{O(\sqrt{\log\log n})}$ term, which was a clear leftover of the older network decomposition complexity [Panconesi and Srinivasan STOC'92]. We present a modified version that remedies this, constructing a decomposition whose quality does not depend on the identifiers, and thus improves the randomized round complexity for various problems.
Quantum algorithms for graph problems with cut queries
Venue: arXiv cs.DS 2020
Year: 2020

Let $G$ be an $n$-vertex graph with $m$ edges. When asked a subset $S$ of vertices, a cut query on $G$ returns the number of edges of $G$ that have exactly one endpoint in $S$. We show that there is a bounded-error quantum algorithm that determines all connected components of $G$ after making $O(\log(n)^6)$ many cut queries. In contrast, it follows from results in communication complexity that any randomized algorithm even just to decide whether the graph is connected or not must make at least $Ω(n/\log(n))$ many cut queries. We further show that with $O(\log(n)^8)$ many cut queries a quantum algorithm can with high probability output a spanning forest for $G$. En route to proving these results, we design quantum algorithms for learning a graph using cut queries. We show that a quantum algorithm can learn a graph with maximum degree $d$ after $O(d \log(n)^2)$ many cut queries, and can learn a general graph with $O(\sqrt{m} \log(n)^{3/2})$ many cut queries. These two upper bounds are tight up to the poly-logarithmic factors, and compare to $Ω(dn)$ and $Ω(m/\log(n))$ lower bounds on the number of cut queries needed by a randomized algorithm for the same problems, respectively. The key ingredients in our results are the Bernstein-Vazirani algorithm, approximate counting with "OR queries", and learning sparse vectors from inner products as in compressed sensing.
A Framework for Threats Analysis Using Software-Defined Networking
Venue: arXiv cs.CR 2020
Year: 2020

The ability to analyze network threats is very important in security research. Traditional approaches, involving sandboxing technology are limited to simulating a single host, missing local network attacks. This issue is addressed by designing a threat analysis framework that uses software-defined networking for simulating arbitrary networks. The presented system offers flexibility, allowing a security researcher to define a virtual network that is able to capture malicious actions and to be restored to the initial state afterwards. Both the framework design and common usage scenarios are described. By providing this framework, we aim to ease the analysis effort in combating cyberthreats.
A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
Venue: arXiv cs.RO 2020
Year: 2020

We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.
Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs
Venue: arXiv cs.CV 2020
Year: 2020

Atmospheric turbulence significantly affects imaging systems which use light that has propagated through long atmospheric paths. Images captured under such condition suffer from a combination of geometric deformation and space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image where prior information regarding the amount of geometric distortion and blur at each location of the face image is first estimated using two separate networks. The estimated prior information is then used by a network called, Turbulence Distortion Removal Network (TDRN), to correct geometric distortion and reduce blur in the face image. Furthermore, a novel loss is proposed to train TDRN where first and second order image gradients are computed along with their confidence maps to mitigate the effect of turbulence degradation. Comprehensive experiments on synthetic and real face images show that this framework is capable of alleviating blur and geometric distortion caused by atmospheric turbulence, and significantly improves the visual quality. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method.
SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
Venue: arXiv cs.CL 2020
Year: 2020

Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words. Existing researches proposed many strategies to integrate lexicon knowledge. However, they performed with simple first-order lexicon knowledge, which provided insufficient word information and still faced the challenge of matched word boundary conflicts; or explored the lexicon knowledge with graph where higher-order information introducing negative words may disturb the identification. To alleviate the above limitations, we present new insight into second-order lexicon knowledge (SLK) of each character in the sentence to provide more lexical word information including semantic and word boundary features. Based on these, we propose a SLK-based model with a novel strategy to integrate the above lexicon knowledge. The proposed model can exploit more discernible lexical words information with the help of global context. Experimental results on three public datasets demonstrate the validity of SLK. The proposed model achieves more excellent performance than the state-of-the-art comparison methods.
SHE-MTJ Circuits for Convolutional Neural Networks
Venue: arXiv cs.ET 2020
Year: 2020

We report the performance characteristics of a notional Convolutional Neural Network based on the previously-proposed Multiply-Accumulate-Activate-Pool set, an MTJ-based spintronic circuit made to compute multiple neural functionalities in parallel. A study of image classification with the MNIST handwritten digits dataset using this network is provided via simulation. The effect of changing the weight representation precision, the severity of device process variation within the MAAP sets and the computational redundancy are provided. The emulated network achieves between 90 and 95\% image classification accuracy at a cost of ~100 nJ per image.